For the past 50 years, biological research has primarily been performed on cell lines and dissociated cell cultures, because these experimental systems are easy to handle in laboratory settings and provide large amounts of material for study. This trend has led to the development of numerous analytical methods that require large amounts of biological material and are readily applicable to cell lines and dissociated cell cultures. For example, Western blot, mass spectrometry, and lysate microarrays all require relatively large amounts of biological material in order to take full advantage of the analytical power of these methods. Large amounts of biological material are usually obtained by simultaneously lysing a large number of dissociated cells in a culture dish. As a consequence of this continued trend, the development of methods for sampling and analyzing single cells in solid tissues has lagged far behind.
One way to potentially access the power of current analytical methods and apply them to single cells, is to reduce the solid-tissue sample to a dissociated culture or a cell suspension by tissue disaggregation. Tissue disaggregation can be achieved by applying collagenase, tripsin, pepsin, papain, elastase and/or pronase to the solid tissue sample for hours in solution, sometimes followed by the trituration of the disaggregated tissue sample (Waymouth, 1974 In Vitro. 10: 97-111; Engelholm et al., 1985 S. A., Spang-Thomsen, M., Brünner, N., Nøhr, I. and Vindeløv, L. L. (1985) Br J Cancer. 51(1): 93-98; Pallavicini, 1987 Techniques in cell cycle analysis. 139-162). In this way, human solid tumors can be reduced to cell suspensions for analysis by flow cytometry (Dalerba et al., 2011, Nat. Biotechnol. 29:1120-1127). Rodent brain tissue can be reduced to dissociated neuronal cultures, from which single neurons can be sampled for RT-qPCR analysis (Morris et al., 2011, J. Vis. Exp. 50: pii: 2634. doi: 10.3791/2634). However, the information about the location of cells is lost after tissue disaggregation. Also, disaggregation of solid tissue might not disperse all cells of the tissue sample. Disaggregation of solid tissue likely kills many native cells, and likely selects certain cell populations over the others. The cell yields of tissue disaggregation vary across tissue types. 1 g of tissue contains approx. 1*109 cells, whereas the typical yields of tissue disaggregation procedures are below 1*108 cells/g (Pallavicini, 1987 Techniques in cell cycle analysis. 139-162). Moreover, the cells that survive tissue disaggregation lose their cell-type-specific biochemistry and functionality due to the lack of the extracellular matrix and due to the changed cellular environment in the culture dish or in the cell suspension. For example, the cell division rates in cancerous tissues and in 3D models are different from the cell division rates observed in 2D cell lines (Fischbach et al., 2007 Nat Methods. 4(10): 855-860). The malignancy of tumors formed by cells cultured in 2D is lower than the malignancy of tumors formed by cells cultured in 3D (Fischbach et al., 2007 Nat Methods 4(10): 855-860). It is also well known that the extracellular matrix of solid tissue plays a critical role in cancer (Bissell et al., 2001 Nat Rev Cancer 1(1):46-54; Hanahan et al., 2000 Cell. 100(1): 57-70). Therefore, dissociated cell cultures and cell suspensions are not equivalent to the original solid tissue.
Most current solid-tissue study methods require fixation. Both laser-capture microdissection and all immunolabeling-based methods (immunofluorescence, FACS, FISH etc.) require fixation (Gutstein et al., 2007, Expert Rev. Proteomics 4:627-637; Espina et al., 2006, Nat. Protoc. 1:586-603; Mouledous et al., 2002, J. Biomol. Tech. 13:258-264; Brandtzaeg and Rognum, 1984, Histochem. Cell Biol. 81:213-219; Micheva and Smith, 2007, Neuron 55:25-36). The latter can provide useful information about the spatial distribution of substrates across the tissue structure and even within cells. The question however is not what can be done with fixed tissue but whether fixed tissue represents the original pre-fixed tissue. If fixed tissue does not represent the original tissue sample, then any study that is based on fixed tissue is not informative. Fixation processes were first documented more than 100 years ago (Fish, 1896, Transactions of the American Microscopical Society, 17:319-330). The process of aldehyde- and alcohol-based fixation is not well understood but is known to undermine the molecular preservation of the original sample, thereby obfuscating the true native differences between single cells (Schnell et al., 2012, Nat. Meth. 9:152-158; Mouledous et al., 2002, J. Biomol. Techniques 13:258-264; Melan and Sluder, 1992, J. Cell Sci. 101:731-743; Holtfreter and Cohen, 1990, Cytometry 11:676-685; Tanaka et al., 2010, Nat. Methods 7:865-866; Collaud et al., 2010, J. Biomol. Tech. 21:25-28). There exists no universal fixation protocol and each specific fixation protocol is exclusively tuned to certain cell types, certain molecular classes, and certain molecules within a molecular class (Schnell et al., 2012, Nat. Meth. 9:152-158; Mouledous et al., 2002, J. Biomol. Techniques 13:258-264; Melan and Sluder, 1992, J. Cell Sci. 101:731-743; Holtfreter and Cohen, 1990, Cytometry 11:676-685).
In order to study the effects of common fixation and permeabilization protocols on molecular preservation of biological samples, Schnell and colleagues expressed cytosol-soluble GFP in 293T and MDCK cells (Schnell et al., 2012, Nat. Meth. 9:152-158). As expected, aldehyde-based fixation led to protein cross-linking and to antigen masking. A large number of GFP molecules in the GFP-expressing cells could not be reached by the applied specific GFP antibodies, even after the extensive permeabilization of these GFP-expressing cells. The same aldehyde-based fixation protocol also led to the spatial redistribution of GFP proteins in fixed MDCK cells, as compared to the same set of MDCK cells imaged before applying aldehyde-based fixation. In contrast to MDCK cells, no spatial redistribution of GFP was observed after fixing 293T cells with the same aldehyde-based fixation protocol. These observations demonstrate that the effects of aldehyde-based fixation on molecular preservation are cell-type dependent. The permeabilization process that is required to access the intracellular proteins in each aldehyde-fixed single cell also led to the extensive extraction of GFP from all fixed single cells (Schnell et al., 2012, Nat. Meth. 9:152-158). Similarly, alcohol-based fixation extracted most GFP proteins from all single cells, as confirmed by fluorescence and electron microscopy (Schnell et al., 2012, Nat. Meth. 9:152-158). Importantly, in the study by Schnell et al., the true GFP quantity differences between single cells in a given set of single cells could not be reproduced by antibody staining of the same set of single cells after fixation and permeabilization, although the applied GFP antibody was specific and correctly detected GFP, when GFP was targeted to the endoplasmatic reticulum.
In another rigorous study by Melan and Sluder, the authors labeled several proteins of different size and charge with fluorescein-5-isothiocyanate (FITC) and then loaded these labeled proteins into HeLa, 3T3, PtK1 and CHO cells (Melan and Sluder, 1992, J. Cell Sci. 101:731-743). They observed that the extent of protein extraction, caused by aldehyde-based fixation and permeabilization, depended both on the particular protein species and on the particular cell type (Melan and Sluder, 1992, J. Cell Sci. 101:731-743). These observations prove that aldehyde- and alcohol-based fixation and permeabilization decrease the analytical availability of native molecules in an unpredictable cell-type and molecule-dependent manner. The results of additional studies examining the effects of fixation and permeabilization on molecular preservation demonstrate that aldehyde- and alcohol-based fixation and permeabilization undermine the molecular preservation of the original sample in an unpredictable cell-type- and molecule-dependent manner (Schnell et al., 2012, Nat. Meth. 9:152-158; Mouledous et al., 2002, J. Biomol. Techniques 13:258-264; Melan and Sluder, 1992, J. Cell Sci. 101:731-743; Holtfreter and Cohen, 1990, Cytometry 11:676-685).
The second major limitation of all fixation-based methods is the difficulty and often the inability of constructing standard curves. A standard curve maps recorded signals to quantities and can be constructed by a concurrent titration series. A standard curve is the basis for any analytical measurement in any discipline. Different affinity-based probes, such as antibodies, usually have different dissociation constants (KD) and thus also have different slopes of their respective standard curves. A large signal difference is meaningless without knowledge of the corresponding standard curve, as it can be the result of a small difference in quantity or the result of a large difference in quantity depending on the slope of the underlying standard curve (FIG. 1). Standard curves also enable absolute measurements, as signals can be mapped to the corresponding absolute counts of the targeted molecules, as well as the correction of non-linear behavior of affinity-based probes at low substrate concentrations in single cells. Standard curves are necessary for pooling data points from different experiments together because the evolution of technology and any variance of experimental procedures can be corrected by the corresponding standard curves.
It is important to note that standard curves have to be concurrent with the actual measurements and have to undergo the same experimental conditions as the measured quantities of interest in the unknown samples. In fixed samples, it is difficult or impossible to construct standard curves. For example, fixation-based solid tissue methods such as immunofluorescence and array tomography (Micheva and Smith, 2007, Neuron 55:25-36) do not allow the construction of concurrent standard curves. In fixed samples, only signals can be seen, but the underlying quantities and/or quantity differences generally cannot be determined.
All fixation-based methods usually suffer from the unpredictable molecular modification of the original sample and from the lack of standard curves. As a consequence of these two major limitations, and as a consequence of the fact that most solid-tissue methods are fixation-based, it has not been possible to date to measure the quantities of native proteins in single cells of solid tissues or to do so in a multiplex manner. It has also not been possible to measure the quantities of metabolites in single cells of solid tissues or to reliably multiplex transcripts in single cells of solid tissues. Multiplexing across molecular classes (proteins, transcripts, metabolites) in single cells of solid tissues has also not been possible.
Although it has been suggested that it is not necessary to measure true quantities or quantity differences to make informative qualitative observations in biology, in reality the correct quantities and quantity differences, as opposed to simply “signals”, are integral to making correct qualitative observations. FIGS. 1 and 2 demonstrate the importance of having concurrent standard curves of affinity-based probes in order to make accurate qualitative observations about the presence or absence of sub-populations in any population measurement (single cells, tissue samples or patients). Given the same true hidden distribution of a quantity of interest across a population, a linear standard curve with a small slope will make this distribution look narrower. In contrast, a linear standard curve with a large slope will make this same true hidden distribution look broader. Given two different affinity-based probes (antibodies for example) with different KD values, and thus with different slopes of their standard curves and given the same true hidden distribution of a quantity, the above-described differences in the observed distributions solely due to the different KD values of the two probes are recorded, although the underlying true distribution of the quantity of interest is the same. Thus, qualitative observations, whether about how broad or how narrow different quantities are distributed in a population, are impossible without the knowledge of the corresponding standard curves.
At the single-cell level, many quantities of proteins, transcripts, or metabolites are present in small numbers, which can result in the non-linearity of the standard curves of the corresponding affinity-based probes. Fixation-induced differential extraction and modification of target molecules also likely result in the non-linearities of the standard curves. FIG. 2 shows how false qualitative observations about the presence or absence of a sub-population can be made in a population measurement if the underlying unknown standard curves are non-linear. Taken together, without knowing the concurrent standard curves of the applied affinity-based probes, it cannot be known if the observed qualitative observations are accurate. Arguably, the lack of concurrent standard curves is the main cause of the irreproducibility and the mutual incompatibility of many biological measurements.
Limited multiplexing is the main limitation of optical methods. The limited optical spectrum leads to the inability of separating tens of signals simultaneously and therefore makes it hard to measure the multivariate molecular mechanisms in single cells by live imaging methods. All live imaging methods are based on intracellular fluorescent probes that inherently perturb the native system of the imaged live cell. For example, one common approach to image proteins in live single cells requires the fusion of GFP derivatives to the protein of interest and the subsequent expression of the resulting fused protein. This procedure is not practical in mammal solid tissues at large scale. The fusion of GFP derivatives to the protein of interest can change both the function of the protein of interest and the native state of the cell (Sigal et al., 2006, Nat. Methods 3:525-531; Landgraf et al., 2012, Nat. Methods 9:480-482; Schnell et al., 2012, Nat. Methods, 3:825-831). The over-expression of such fused proteins and their dimerization are common. The diffusion coefficient and the kinetic parameters of GFP-fused proteins also likely change. Therefore, fused GFP does not directly report the abundances and activity of native proteins. Fluorescent intracellular ion sensors are another example of how intracellular fluorescent probes perturb the native system of the imaged cell (Yasuda et al., 2004, Sci STKE. 219: p 15). Fluorescent intracellular ion sensors are chelators and thus perturb the native system of the cell by changing the native concentrations of the respective ions (such as Ca2+).
There is a need in the art for methods that examine single cell components derived from live solid tissues where the methods preserve the components of the single cell in analytically defined or natural state. The present invention addresses this unmet need in the art.